Online distributed learning-based load-aware heterogeneous vehicular edge computing

Online Function Scheduling for Dual-Heterogeneous Serverless Vehicular Edge Computing

L Zhu, H Huang, Z Zhang, L Zhuang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Vehicular service providers bear a heavy burden of scalability and service load balancing
problems in traditional edge computing systems. By abstracting the service computing …
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Verifiable Deep Learning Inference on Heterogeneous Edge Devices with Trusted Execution Environment

L Liao, Y Zheng, H Lu, X Liu, S Chen… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
Deep learning inference on edge devices is susceptible to security threats, particularly fault
injection attacks (FIAs), which are easily executed and pose a significant risk to the …
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System-Wide Energy Efficient Computation Offloading in Vehicular Edge Computing With Speed Adjustment

H Li, X Li, M Zhang, B Ulziinyam - IEEE Transactions on Green …, 2024 - ieeexplore.ieee.org
Vehicle-to-everything (V2X) communications in future 6G intelligent transportation systems
are expected to enable various convenience applications which consume amount of …
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[PDF] arxiv.org

Delay-Effective Task Offloading Technology in Internet of Vehicles: From the Perspective of the Vehicle Platooning

K Yu, F Zhu, X Liu, Z Feng, D Li - arXiv preprint arXiv:2405.16060, 2024 - arxiv.org
The task offloading technology plays a crucial vital role in the Internet of Vehicle (IoV) with
the demands of delay minimum, by jointly optimizing the heterogeneous computing …
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Offloading in V2x with Road Side Units: Deep Reinforcement Learning

W Yahya, YD Lin, F Marzuk, P Chołda… - Available at SSRN … - papers.ssrn.com
Traffic offloading is vital to decrease the computing latency in distributed edge systems such
as a vehicle-to-everything (V2X) system with a roadside unit (RSU) and access network …
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